Instructions to use LiquidAI/d1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/d1-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="LiquidAI/d1-3B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/d1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/d1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/d1-3B

SGLang

How to use LiquidAI/d1-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/d1-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/d1-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/d1-3B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/d1-3B
```

d1-3B / README.md

mlabonne

Iwaku-Real

Correct citation link in README.md (#6)

051bcc4 1 day ago

preview code

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- ar
	- zh
	- en
	- fr
	- de
	- hi
	- id
	- it
	- ja
	- ko
	- pl
	- pt
	- ru
	- es
	- th
	- vi
	pipeline_tag: image-text-to-text
	base_model: LiquidAI/LFM2.5-VL-3B
	tags:
	- liquid
	- lfm2.5
	- edge
	- decision
	- classification
	- calibration
	- system-one
	- multimodal
	- decision-model
	---

	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
	<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
	<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
	<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</div>

	# d1-3B

	d1-3B is a 3B parameter decision model built on [LFM2.5-VL-3B](https://huggingface.co/LiquidAI/LFM2.5-VL-3B).
	You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated,
	typed answers in one forward pass with zero output tokens.

	- Best decision model under 10B on the Decision Index 0.2.1: 48.57, ahead of every 4B and 9B model
	and of Decider 35B-A3B (47.11).
	- Multimodal: images and text in the same state. It scores 74.1 on 11 public image benchmarks
	(LFM2.5-VL-3B: 73.9).
	- Fast: 8 ms a decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro.

	Find more information about open d1 in our [blog post](https://www.liquid.ai/blog/open-d1).

	![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/r2H7UlZ_m48SYAhZWIHiu.png)

	> [!NOTE]
	> 💻 Demos: Try d1-3B in a Hugging Face space without any setup:
	> [Open d1 Arcade](https://huggingface.co/spaces/LiquidAI/system-one-arcade): Collection of 10 demos using d1-3B


	## 🗒️ Model Details

	\| Model \| Parameters \| Description \|
	\|---\|---\|---\|
	\| [LFM2.5-VL-3B](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) \| 3.1B \| General-purpose vision-language model (base) \|
	\| [d1-3B](https://huggingface.co/LiquidAI/d1-3B) \| 3.1B \| Post-trained for single-pass, calibrated decisions \|

	d1-3B is a multimodal decision model with the following features:

	- Total parameters: 3.12B
	- Vision encoder: SigLIP2 NaFlex shape-optimized 400M
	- Context length: 32,768 tokens
	- Vocabulary size: 128,000

	We recommend d1-3B wherever a pipeline needs a yes/no, a pick from named options, or a rating:
	routing and triage, moderation, intent and topic classification, extraction checks, reranking, LLM-as-a-judge
	scoring, agent guardrails, and visual inspection. It is not a chat model and does not write text.

	## 🏃 How to use

	Install the dependencies (requires `transformers>=5.14`):

	```bash
	pip install "transformers>=5.14" torch torchvision pillow
	```

	The model ships its own code, so load it with `trust_remote_code=True`:

	```python
	import torch
	from transformers import AutoModel
	from transformers.image_utils import load_image

	device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
	dtype = torch.float32 if device == "cpu" else torch.bfloat16
	model = AutoModel.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, dtype=dtype).to(device)

	# Text: several named questions over one state, answered in one pass
	questions = {
	"refund": {
	"type": "noul",
	"instructions": "Is the customer asking for a refund?",
	},
	"team": {
	"type": "choice",
	"instructions": "Which team should handle this?",
	"criteria": {
	"billing": "Charges, refunds, invoices",
	"technical": "App or site faults",
	"fraud": "Suspected unauthorised use",
	},
	},
	"urgency": {
	"type": "score",
	"instructions": "How urgent is this?",
	"criteria": ["Can wait", "Today", "Blocking the customer now"],
	},
	}
	print(model.system_one("I was charged twice this month, please refund one of them.", questions))

	# Image: the photo is the whole state
	image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg") # two cats on a sofa
	cats = {
	"type": "choice",
	"instructions": "How many cats are there?",
	"criteria": {"one": "One", "two": "Two", "more": "Three or more"},
	}
	print(model.system_one(None, {"cats": cats}, images=[image]))

	# Batch: many requests, packed together with no padding
	tickets = ["Where is my parcel? It was due Monday.", "The app crashes when I open settings."]
	print(model.system_one_batch([(t, {"team": questions["team"]}) for t in tickets]))
	```

	\| call \| \|
	\|---\|---\|
	\| `system_one(state, questions, images=None)` \| Named questions over one state, in one pass. The state and its images are read once for all questions. \|
	\| `system_one_batch([(state, questions[, images]), ...])` \| Many requests, packed with no padding. \|

	A state is a string, any JSON value, or `None` when the images are the whole state.

	### Questions and answers

	Questions follow the Decision Index schema: `type`, `instructions`, and `criteria`.

	\| `type` \| `criteria` \| answer fields \|
	\|---\|---\|---\|
	\| `noul`: yes or no \| optional: `{"true": "...", "false": "..."}` to define each side \| `noul`: P(yes) \|
	\| `choice`: one of named options \| `{name: description}` \| `choice`, `confidence`, `probabilities` \|
	\| `score`: 2 to 10 ordered levels \| a list of level descriptions, lowest first \| `score` (the expected level), `confidence`, `probabilities`, `legend` \|

	Each call returns `{"answers": {name: answer}, "usage": {"input_tokens": n, "output_tokens": 0}}`.

	## ⚡ Speed

	Warm calls, one request at a time: a single question, three questions over one state, a 3.4k-token
	state and a 384 px image. The last column is throughput with 64 states packed into one pass.

	### Edge Inference

	We measure latency on an Apple M5 Pro and, in collaboration with NVIDIA, on an NVIDIA Jetson AGX Thor,
	a Jetson AGX Orin 64 GB and a Jetson Orin Nano.

	\| \| one question \| 3 questions, one pass \| 3.4k-token state \| 384 px image \| 64 states, packed \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| Apple M5 Pro (`mps`) \| 30 ms \| 41 ms \| 640 ms \| 62 ms \| 78 / s \|
	\| NVIDIA Jetson AGX Thor \| 16 ms \| 20 ms \| 220 ms \| 35 ms \| 262 / s \|
	\| NVIDIA Jetson AGX Orin 64 GB \| 26 ms \| 35 ms \| 560 ms \| 83 ms \| 110 / s \|
	\| NVIDIA Jetson Orin Nano \| 50 ms \| 73 ms \| 1640 ms \| 202 ms \| 38 / s \|

	### GPU Inference

	We measure latency on an NVIDIA RTX 4090 and an AMD MI325X, in bf16, median of 20 runs.

	\| \| one question \| 3 questions, one pass \| 3.4k-token state \| 384 px image \| 64 states, packed \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| NVIDIA RTX 4090 \| 8 ms \| 21 ms \| 102 ms \| 17 ms \| 475 / s \|
	\| AMD MI325X \| 9 ms \| 14 ms \| 44 ms \| 18 ms \| 1,106 / s \|

	On NVIDIA GPUs, `model.compile(mode="reduce-overhead")` runs single questions as CUDA graphs (the RTX 4090
	row uses it). Without it, a single question takes 16 ms. The first call with a new shape pays for kernel
	selection or compilation, so warm up the shapes you serve.

	## 📊 Performance

	All results are on public benchmarks.

	### Decision Index 0.2.1

	We scored d1-3B with the official scorer (not a leaderboard submission). All other rows come from the public leaderboard v0.2.1.

	\| Model \| Size \| Decision Index \| Knowledge \| Language \| Retrieval \| Tools \| Arts \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| Winnow-12B \| 12B \| 50.02 \| 33.8 \| 56.0 \| 54.0 \| 71.0 \| 30.0 \|
	\| d1-3B \| 3B \| 48.57 \| 23.8 \| 56.4 \| 52.8 \| 74.5 \| 36.3 \|
	\| Decider 35B-A3B \| 36B \| 47.11 \| 31.8 \| 55.5 \| 54.7 \| 56.5 \| 32.6 \|
	\| JPT-9B \| 9.7B \| 46.89 \| 31.7 \| 56.7 \| 44.6 \| 67.0 \| 28.6 \|
	\| Decision 1.0 Lux \| 9.7B \| 43.49 \| 30.9 \| 48.0 \| 50.0 \| 57.2 \| 26.4 \|
	\| JPT-4B \| 4.7B \| 43.04 \| 28.7 \| 52.5 \| 45.0 \| 57.2 \| 25.8 \|
	\| Jet v6.2 \| 4.7B \| 42.60 \| 28.7 \| 43.9 \| 48.2 \| 62.9 \| 27.0 \|
	\| Decider 4B \| 4.7B \| 40.70 \| 25.7 \| 46.0 \| 44.7 \| 58.6 \| 25.0 \|
	\| Winnow-E4B \| 8.0B \| 39.89 \| 22.3 \| 45.1 \| 43.8 \| 62.5 \| 22.8 \|
	\| Decider 2B \| 2.3B \| 28.97 \| 14.9 \| 32.6 \| 37.3 \| 42.4 \| 14.6 \|

	### Benchmarks as decisions

	Besides the Decision Index, we added a few other internal evaluations based on public benchmarks.

	\| Benchmark \| d1-3B \| Decider 4B \| Decider 2B \|
	\|---\|---:\|---:\|---:\|
	\| SQuAD 2.0 \| 85.3 \| 76.0 \| 67.7 \|
	\| Civil Comments \| 93.0 \| 92.8 \| 93.6 \|
	\| MASSIVE intent \| 87.3 \| 88.3 \| 81.1 \|
	\| HelpSteer2 \| 36.7 \| 42.0 \| 32.0 \|
	\| PubMedQA \| 66.0 \| 63.3 \| 65.7 \|
	\| BoolQ \| 86.7 \| 89.0 \| 87.3 \|
	\| XNLI \| 85.0 \| 88.6 \| 85.0 \|
	\| PAWS-X \| 76.9 \| 69.8 \| 59.5 \|
	\| Mean \| 77.1 \| 76.2 \| 71.5 \|

	d1-3B also scores 71.8 on [DecisionBench](https://huggingface.co/datasets/Hanno-Labs/decision-bench) (eng v1,
	all 23,900 rows) and 69.3 on [Fast Decisions](https://huggingface.co/datasets/fastino/fast-decisions)
	(dev split).

	### Vision

	Eleven public image benchmarks, read as decisions over each benchmark's options (at most 1,000 rows each),
	compared with the base model:

	\| Benchmark \| d1-3B \| LFM2.5-VL-3B \|
	\|---\|---:\|---:\|
	\| AI2D \| 79.9 \| 80.9 \|
	\| BLINK \| 59.2 \| 58.7 \|
	\| CV-Bench \| 82.1 \| 87.6 \|
	\| HallusionBench \| 65.3 \| 65.0 \|
	\| MMBench \| 84.9 \| 84.3 \|
	\| MME \| 82.1 \| 82.4 \|
	\| MMStar \| 59.9 \| 61.2 \|
	\| MMVP \| 77.0 \| 73.7 \|
	\| POPE \| 88.5 \| 90.1 \|
	\| VisualWebBench \| 71.4 \| 78.3 \|
	\| VL-RewardBench \| 65.0 \| 50.9 \|
	\| Mean \| 74.1 \| 73.9 \|
	\| [ImajevBench](https://huggingface.co/datasets/mohit67890/imajev-bench) (dev and calibration, 253 rows) \| 64.0 \| 66.8 \|

	With the images removed, the same questions score 45.1, so the answers come from the images.

	## 📬 Contact

	- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
	- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

	## Citation

	```bibtex
	@article{liquidAI2026opend1,
	author = {Liquid AI},
	title = {Open d1: Edge decision models for text, vision, and audio},
	journal = {Liquid AI Blog},
	year = {2026},
	note = {https://www.liquid.ai/blog/d1-open},
	}
	```

	```bibtex
	@article{liquidai2025lfm2,
	title = {LFM2 Technical Report},
	author = {Liquid AI},
	journal = {arXiv preprint arXiv:2511.23404},
	year = {2025}
	}
	```